November 2022 arXiv papers — page 77

Lin Cheng, Bernardo A. Huberman

We propose an auction-based mechanism to improve the efficiency of low earth orbit satellite communication systems. The mechanism allows the ground stations to bid for downlink resources such as spectrum, satellite links, or radios, without the need to send channel status back to satellites. Simulation and experimental results show that this mechanism improv

Anthony Simeonov, Yilun Du, Lin Yen-Chen, Alberto Rodriguez

We present a method for performing tasks involving spatial relations between novel object instances initialized in arbitrary poses directly from point cloud observations. Our framework provides a scalable way for specifying new tasks using only 5-10 demonstrations. Object rearrangement is formalized as the question of finding actions that configure task-rele

K. L. Luhman

I have used high-precision photometry and astrometry from the third data release of Gaia (DR3) to perform a survey for members of the Taurus star-forming region and young associations in its vicinity. This work has produced a new catalog of 532 adopted members of Taurus, which has only minor changes relative to the previous catalog from Esplin & Luhman 2019.

Shiyu Zhou, Maria Zelenayova, Oliver Hart, Claudio Chamon

Recent advances in programmable quantum devices brought to the fore the intriguing possibility of using them to realise and investigate topological quantum spin liquid phases. This new and exciting direction brings about important research questions on how to probe and determine the presence of such exotic, highly entangled phases. One of the most promising

Yulong Chen, Yang Liu, Ruochen Xu, Ziyi Yang

The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization. However, despite the emergence of many summarization tasks and datasets, the current training paradigm for few-shot summarization systems ignores potentially shareable knowledge in heterogeneous datasets. To this end, we propose \t

Peter Ebert Christensen, Vésteinn Snæbjarnarson, Andrea Dittadi, Serge Belongie

The robustness of image classifiers is essential to their deployment in the real world. The ability to assess this resilience to manipulations or deviations from the training data is thus crucial. These modifications have traditionally consisted of minimal changes that still manage to fool classifiers, and modern approaches are increasingly robust to them. S

Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick

Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to pr

Yicheng Bao, Scarlett S. Yu, Loïc Anderegg, Eunmi Chae

Due to their intrinsic electric dipole moments and rich internal structure, ultracold polar molecules are promising candidate qubits for quantum computing and for a wide range of quantum simulations. Their long-lived molecular rotational states form robust qubits while the long-range dipolar interaction between molecules provides quantum entanglement. Using

Edward Frenkel, David Hernandez

We define an action of the Weyl group W of a simple Lie algebra g on a completion of the ring Y, which is the codomain of the q-character homomorphism of the corresponding quantum affine algebra U_q(g^). We prove that the subring of W-invariants of Y is precisely the ring of q-characters, which is isomorphic to the Grothendieck ring of the category of finite

Sophia Gu, Christopher Clark, Aniruddha Kembhavi

Many high-level skills that are required for computer vision tasks, such as parsing questions, comparing and contrasting semantics, and writing descriptions, are also required in other domains such as natural language processing. In this paper, we ask whether it is possible to learn those skills from text data and then transfer them to vision tasks without e

Bo-Ye Sun, Nathan Goldman, Monika Aidelsburger, Marin Bukov

We propose a scheme to implement Kitaev's honeycomb model with cold atoms, based on a periodic (Floquet) drive, in view of realizing and probing non-Abelian chiral spin liquids using quantum simulators. We derive the effective Hamiltonian to leading order in the inverse-frequency expansion, and show that the drive opens up a topological gap in the spectrum w

Lap Chi Lau, Kam Chuen Tung, Robert Wang

We derive Cheeger inequalities for directed graphs and hypergraphs using the reweighted eigenvalue approach that was recently developed for vertex expansion in undirected graphs [OZ22,KLT22,JPV22]. The goal is to develop a new spectral theory for directed graphs and an alternative spectral theory for hypergraphs. The first main result is a Cheeger inequality

Mustafa A. Amin, Mehrdad Mirbabayi

We argue that there is a lower bound of order $10^{-19}$ eV on dark matter mass if it is produced after inflation via a process with finite correlation length. We rely on non-detection of free-streaming suppression and white-noise enhancement of density perturbations as the observational inputs.

Allahkaram Shafiei, Jakub Marecek

Multi-objective optimization studies the process of seeking multiple competing desiderata in some operation. Solution techniques highlight marginal tradeoffs associated with weighing one objective over others. In this paper, we consider time-varying multi-objective optimization, in which the objectives are parametrized by a continuously varying parameter and

Christian Elsholtz, Gabriel F. Lipnik

In spite of a recent breakthrough on upper bounds of the size of cap sets (by Croot, Lev and Pach (2017) and Ellenberg and Gijswijt (2017)), the classical cap set constructions had not been affected. In this work, we introduce a very different method of construction for caps in all affine spaces with odd prime modulus $p$. Moreover, we show that for all prim

Amaya Dharmasiri, Dinithi Dissanayake, Mohamed Afham, Isuru Dissanayake

3D generative models have been recently successful in generating realistic 3D objects in the form of point clouds. However, most models do not offer controllability to manipulate the shape semantics of component object parts without extensive semantic attribute labels or other reference point clouds. Moreover, beyond the ability to perform simple latent vect

Ahmed Alkhateeb, Gouranga Charan, Tawfik Osman, Andrew Hredzak

This article presents the DeepSense 6G dataset, which is a large-scale dataset based on real-world measurements of co-existing multi-modal sensing and communication data. The DeepSense 6G dataset is built to advance deep learning research in a wide range of applications in the intersection of multi-modal sensing, communication, and positioning. This article

Peco Myint, Miaoqi Chu, Ashish Tripathi, Michael J. Wojcik

To study nanostructures on substrates, surface-sensitive reflection-geometry scattering techniques such as grazing incident small angle x-ray scattering are commonly used to yield an averaged statistical structural information of the surface sample. Grazing incidence geometry can probe the absolute three-dimensional structural morphology of the sample if a h

Xiaokang Chen, Jiahui Chen, Yan Liu, Gang Zeng

While various knowledge distillation (KD) methods in CNN-based detectors show their effectiveness in improving small students, the baselines and recipes for DETR-based detectors are yet to be built. In this paper, we focus on the transformer decoder of DETR-based detectors and explore KD methods for them. The outputs of the transformer decoder lie in random

Antoine Pfaff, Hubert Hansen, Juan M. Torres-Rincon, Joerg Aichelin

We investigate the capabilities of the Nambu--Jona-Lasinio model to describe and reproduce fundamental vacuum properties of Quantum Chromodynamics, notably the hadronic spectrum. Mesons are described as quark-antiquark bound states at the level of the random phase approximation of the Bethe-Salpeter equation, while baryons are characterized as quark-diquark

Félix Werner, Xavier Leyronas

We consider the resonant Fermi gas, that is, two-component fermions in three dimensions interacting by a short-range potential of large scattering length. We introduce a quantity, the three-body contact, that determines several observables. Within the zero-range model, the number of nearby fermion triplets, the large-momentum tail of the center-of-mass momen

Rauan Kaldybayev

Newton's quadrilateral theorem can be phrased as follows. If H is a circle that is tangent to the four extended sides of a non-parallelogram quadrilateral Q, the center of H lies on the Newton line of Q. We prove that the theorem remains true if H is an arbitrary hyperbola or ellipse. A quadrilateral can have at most one circle tangent to it but infinitely m

Sören Kleine, Katharina Müller

We investigate the growth of the $p$-part of the Jacobians in voltage covers of finite connected multigraphs, where the voltage group is isomorphic to $\mathbb{Z}_p^l$ for some ${l \ge 2}$, and we study analogues of a conjecture of Greenberg on the growth of class numbers in multiple $\mathbb{Z}_p$-extensions of number fields. Moreover we prove an Iwasawa ma

Akira Kyle, Curtis L. Rau, William D. Warfield, Alex Kwiatkowski

Doubly-parametric quantum transducers (DPTs), such as electro-opto-mechanical devices, show promise as quantum interconnects between the optical and microwave domains, thereby enabling long distance quantum networks between superconducting qubit systems. However, any transducer will inevitably introduce loss and noise that will degrade the performance of a q

Piotr Nawrot, Jan Chorowski, Adrian Łańcucki, Edoardo M. Ponti

Transformers achieve unrivalled performance in modelling language, but remain inefficient in terms of memory and time complexity. A possible remedy is to reduce the sequence length in the intermediate layers by pooling fixed-length segments of tokens. Nevertheless, natural units of meaning, such as words or phrases, display varying sizes. To address this mis

Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant

While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers. In this work, we leverage the same scaling approach behind the success of deep learning to learn versatile optimizers. We train an optimizer for deep learning which is itself a small neural network that ingests gr

Vyacheslav M. Boyko, Roman O. Popovych, Oleksandra O. Vinnichenko

Applying an original megaideal-based version of the algebraic method, we compute the point-symmetry pseudogroup of the dispersionless (potential symmetric) Nizhnik equation. This is the first example of this kind in the literature, where there is no need to use the direct method for completing the computation. The analogous studies are also carried out for t

Zongxin Yu, Ivan C. Christov

A ferrofluid droplet confined in a Hele-Shaw cell can be deformed into a stably spinning ``gear,'' using crossed magnetic fields. Previously, fully nonlinear simulation revealed that the spinning gear emerges as a stable traveling wave along the droplet's interface bifurcates from the trivial (equilibrium) shape. In this work, a center manifold reduction is

Tomas Codina, Olaf Hohm, Diego Marques

We explore the exactly duality invariant higher-derivative extension of double field theory due to Hohm, Siegel and Zwiebach (HSZ) specialized to cosmological backgrounds. Despite featuring a finite number of derivatives in its original formulation, this theory encodes infinitely many $\alpha'$ corrections for metric, B-field and dilaton, which are obtained

Andrew Vlasic, Hunter Grant, Salvatore Certo

Feature selection is a technique in statistical prediction modeling that identifies features in a record with a strong statistical connection to the target variable. Excluding features with a weak statistical connection to the target variable in training not only drops the dimension of the data, which decreases the time complexity of the algorithm, it also d

Viraj Pandya, Drummond B. Fielding, Greg L. Bryan, Christopher Carr

The circumgalactic medium (CGM) plays a pivotal role in regulating gas flows around galaxies and thus shapes their evolution. However, the details of how galaxies and their CGM co-evolve remain poorly understood. We present a new time-dependent two-zone model that self-consistently tracks not just mass and metal flows between galaxies and their CGM but also

V. Rezazadeh, R. H. J. Peerlings, F. Maresca, J. P. M. Hoefnagels

In particular types of layer- or lamellar-like microstructures such as pearlite and lath martensite, plastic slip occurs favorably in directions parallel to inter-lamellar boundaries. This may be due to the interplay between morphology and crystallographic orientation or, more generally, due to constraints imposed on the plastic slip due to the lamellar micr

Paulo Areyuna C., Jilberto Zamora-Saa, Alfonso R. Zerwekh

In this work, we consider an extension to the Standard Model composed by a Massive Vector Doublet under SU(2)$_L$ and a Left-handed Heavy Neutral Lepton. We study the production of these exotic leptons with the Same Flavor Opposite Sign standard lepton pair, and jets, considering Drell-Yan and Vector Boson Fusion as independent cases. We find that for the la

Yuanshun Yao, Chong Wang, Hang Li

Recommender system practitioners are facing increasing pressure to explain recommendations. We explore how to explain recommendations using counterfactual logic, i.e. "Had you not interacted with the following items, we would not recommend it." Compared to the traditional explanation logic, counterfactual explanations are easier to understand, more technical

Nayeeb Rashid, Swapnil Saha, Mohseu Rashid Subah, Rizwan Ahmed Robin

Cardiovascular diseases are one of the leading cause of death in today's world and early screening of heart condition plays a crucial role in preventing them. The heart sound signal is one of the primary indicator of heart condition and can be used to detect abnormality in the heart. The acquisition of heart sound signal is non-invasive, cost effective and r

Philipp Grete, Brian W. O'Shea, Kris Beckwith

Magnetized turbulence is ubiquitous in many astrophysical and terrestrial plasmas but no universal theory exists. Even the detailed energy dynamics in magnetohydrodynamic (MHD) turbulence are still not well understood. We present a suite of subsonic, super-Alfv\'enic, high plasma-beta MHD turbulence simulations that only vary in their dynamical range, i.e.,

Abdon Moutinho

We study the elasticity of the collision of two kinks with an incoming low speed $v\in (0,1)$ for the nonlinear wave equation in dimension $1+1$ known as the $\phi^{6}$ model. We prove for any $k\in\mathbb{N}$ that if the incoming speed $v$ is small enough, then, after the collision, the two kinks will move away with a velocity $v_{f}$ such that $\vert v_{f}

Tiwalayo Eisape, Vineet Gangireddy, Roger P. Levy, Yoon Kim

Next-word predictions from autoregressive neural language models show remarkable sensitivity to syntax. This work evaluates the extent to which this behavior arises as a result of a learned ability to maintain implicit representations of incremental syntactic structures. We extend work in syntactic probing to the incremental setting and present several probe

Ishan Bansal, Joseph Cheriyan, Logan Grout, Sharat Ibrahimpur

We present approximation algorithms for network design problems in some models related to the $(p,q)$-FGC model. Adjiashvili, Hommelsheim and M\"uhlenthaler introduced the model of Flexible Graph Connectivity that we denote by FGC. Boyd, Cheriyan, Haddadan and Ibrahimpur introduced a generalization of FGC. Let $p\geq 1$ and $q\geq 0$ be integers. In an insta

Junshi Chen, Russ Whiton, Xuhong Li, Fredrik Tufvesson

Accurate understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink l

Yueshui Zhang, Anton Hulsch, Hua-Chen Zhang, Wei Tang

We show that the Klein bottle entropy [Phys. Rev. Lett. 119, 261603 (2017)] for conformal field theories (CFTs) perturbed by a relevant operator is a universal function of the dimensionless coupling constant. The universal scaling of the Klein bottle entropy near criticality provides an efficient approach to extract the scaling dimension of lattice operators

Yousef El-Kurdi, Jerry Quinn, Avirup Sil

We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing models either modify the training procedure,or they require an additional calibration step to adjust parameters that also requires a selected held-out dataset. Our method permits taki

Adolfo O. Fumega, Josu Diego, V. Pardo, S. Blanco-Canosa

VSe$_2$ is a layered compound that has attracted great attention due to its proximity to a ferromagnetic state that is quenched by the presence of a charge density wave (CDW) phase. In the monolayer limit, unrelated experiments have reported different CDW orders with transition temperatures in the range of 130 to 220 K, making this monolayer very controversi

Arthur Filoche, Julien Brajard, Anastase Charantonis, Dominique Béréziat

Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive biases to learn complex relationships. We here design a hybrid architecture learning the assimilation task directly from partial and noisy observations, using the mechanistic constr

Mehrtash Mehrabi, Yingxue Zhang

One of the challenges in studying the interactions in large graphs is to learn their diverse pattern and various interaction types. Hence, considering only one distribution and model to study all nodes and ignoring their diversity and local features in their neighborhoods, might severely affect the overall performance. Based on the structural information of

G. Dong, S. L. Liang, A. Sakanas, E. Semenova

A microlaser with low energy consumption and high speed is crucial for on-chip photonic networks. Presently, the modulation of microlasers is based on modulating the gain of the laser, which implies a trade-off between the output peak power and the modulation energy. Besides, the temporal width of the output pulse is restricted by the carrier relaxation time

Sarvesh Mangla, Sumanjit Chakraborty, Abhirup Datta, Ashik Paul

The Earth's ionosphere introduces systematic effects that limit the performance of a radio interferometer at low frequencies ($\lesssim 1$\,GHz). These effects become more pronounced for severe geomagnetic activities or observations involving longer baselines of the interferometer. The uGMRT, a pathfinder for the Square Kilometre Array (SKA), is located in b

Julien Boulanger, Sam Freedman

We complete the work of Lanneau and M\"oller to show that there are no primitive Teichm\"uller curves in Prym(2,2).

Mark Braverman, Dor Minzer

A regular graph $G = (V,E)$ is an $(\varepsilon,\gamma)$ small-set expander if for any set of vertices of fractional size at most $\varepsilon$, at least $\gamma$ of the edges that are adjacent to it go outside. In this paper, we give a unified approach to several known complexity-theoretic results on small-set expanders. In particular, we show: 1. Max-Cut:

Francesco Petiziol, Sandro Wimberger, André Eckardt, Florian Mintert

We theoretically propose a quantum simulation scheme for the toric-code Hamiltonian, the paradigmatic model of a quantum spin liquid, based on time-periodic driving. We develop a hybrid continuous-digital strategy that exploits the commutativity of different terms in the target Hamiltonian. It allows one to realize the required four-body interactions in a no

Jiaxin Shi, Lester Mackey

We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability distribution with a collection of particles. Specifically, whenever the target distribution is sub-Gaussian with a Lipschitz score, SVGD with n particles and an appropriate step size sequence drives the kern

V. Rezazadeh, R. H. J. Peerlings, J. P. M. Hoefnagels, M. G. D. Geers

Experimental findings in the literature reveal that DP steels with identical strength and composition, but different microstructures, exhibit inconsistent trends under either necking-controlled or damage-controlled ductility tests. A special case of this phenomenon is referred to in the forming community as the cut-edge failure or edge cracking issue of DP s

Ziyao Wang, Thai Le, Dongwon Lee

Consider a scenario where an author-e.g., activist, whistle-blower, with many public writings wishes to write "anonymously" when attackers may have already built an authorship attribution (AA) model based off of public writings including those of the author. To enable her wish, we ask a question "Can one make the publicly released writings, T, unattributable

Nuno Guedelha, Venus Pasandi, Giuseppe L'Erario, Silvio Traversaro

Physics simulators are widely used in robotics fields, from mechanical design to dynamic simulation, and controller design. This paper presents an open-source MATLAB/Simulink simulator for rigid-body articulated systems, including manipulators and floating-base robots. Thanks to MATLAB/Simulink features like MATLAB system classes and Simulink function blocks

Abdon Moutinho

This manuscript is the first of a series of two papers that study the problem of elasticity and stability of the collision of two kinks with low speed $v$ for the nonlinear wave equation known as the $\phi^{6}$ model in dimension $1+1$. In this paper, we construct a sequence of approximate solutions $(\phi_{k}(v,t,x))_{k\in\mathbb{N}_{\geq 2}}$ for this nonl

Shlomo Tannor, Nachum Dershowitz, Moshe Lavee

Midrash collections are complex rabbinic works that consist of text in multiple languages, which evolved through long processes of unstable oral and written transmission. Determining the origin of a given passage in such a compilation is not always straightforward and is often a matter of dispute among scholars, yet it is essential for scholars' understandin

Thuy Nuong Tran, Tim Adler, Amine Yamlahi, Evangelia Christodoulou

Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for understanding metric-related pitfalls in image analysis problems, there is a lack of experimental evidence on the concret

Simon Alexanderson, Rajmund Nagy, Jonas Beskow, Gustav Eje Henter

Diffusion models have experienced a surge of interest as highly expressive yet efficiently trainable probabilistic models. We show that these models are an excellent fit for synthesising human motion that co-occurs with audio, e.g., dancing and co-speech gesticulation, since motion is complex and highly ambiguous given audio, calling for a probabilistic desc

Yulin Wang, Yang Yue, Rui Lu, Tianjiao Liu

The superior performance of modern deep networks usually comes with a costly training procedure. This paper presents a new curriculum learning approach for the efficient training of visual backbones (e.g., vision Transformers). Our work is inspired by the inherent learning dynamics of deep networks: we experimentally show that at an earlier training stage, t

Abha Tripathi, S. P. Tiwari, J. Kavikumar

This contribution introduces the concept of granular F-transform and investigates its basic properties by using the theory of fuzzy numbers and horizontal membership functions. Further, we present a numerical method based on granular F-transform to solve a fuzzy prey-predator model consisting of two prey and one predator due to its natural variability and in

Christopher Madrid, Ryan Heller, Claudio San Martín, Shirsendu Nanda

We present the first beam test results with centimeter-scale AC-LGAD strip sensors, using the Fermilab Test Beam Facility and sensors manufactured by the Brookhaven National Laboratory. Sensors of this type are envisioned for applications that require large-area precision 4D tracking coverage with economical channel counts, including timing layers for the El

Alexis Groshenry, Clement Giron, Thomas Lauvaux, Alexandre d'Aspremont

The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m). We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas. To co

J. L. Tous, H. Domínguez-Sánchez, J. M. Solanes, J. D. Perea

Spatially resolved integral field spectroscopic maps in a sample of $532$ S0 galaxies from the MaNGA survey have unveiled the existence of inner rings ($\langle R\rangle\sim 1\,R_\mathrm{e}$) betraying ongoing star formation in a number of these objects. Activity gradients averaged over bins of galactocentric radius up to $\sim 1.5\,R_\mathrm{e}$ have been m

Adriano Barra, Giovanni Catania, Aurélien Decelle, Beatriz Seoane

In this paper we investigate the equilibrium properties of bidirectional associative memories (BAMs). Introduced by Kosko in 1988 as a generalization of the Hopfield model to a bipartite structure, the simplest architecture is defined by two layers of neurons, with synaptic connections only between units of different layers: even without internal connections

Alison A. Silva, Fabiano M. Andrade, D. Bazeia

This work deals with the scattering entropy of quantum graphs in many different circumstances. We first consider the case of the Shannon entropy and then the R\'enyi and Tsallis entropies, which are more adequate to study distinct quantitative behavior such as entanglement and nonextensive behavior, respectively. We describe many results associated with diff

Erik Aurell, David Machado Perez, Roberto Mulet

We consider classical spin systems evolving in continuous time with interactions given by a locally tree-like graph. Several approximate analysis methods have earlier been reported based on the idea of Belief Propagation / cavity method. We introduce a new such method which can be derived in a more systematic manner, and which performs better on several impo

Amanda Xu, Abtin Molavi, Lauren Pick, Swamit Tannu

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modificati

Matthew G. Smith, Jack Radford, Eky Febrianto, Jorge Ramírez

It has been argued [Tassieri, \textit{Soft Matter}, 2015, \textbf{11}, 5792] that linear microrheology with optical tweezers (MOT) of living systems ``\textit{is not an option}'', because of the wide gap between the observation time required to collect statistically valid data and the mutational times of the organisms under study. Here, we have taken a first

M. Bashkanov, D. P. Watts, S. J. D. Kay, S. Abt

We report a first measurement of the double-polarisation observable, $C_{x'}$, in $\pi^+$ photoproduction off the proton. The $C_{x'}$ double-polarisation observable represents the transfer of polarisation from a circularly polarised photon beam to the recoiling neutron. The MAMI circularly polarised photon beam impinged on a liquid deuterium target cell, wi

Dhritiman Bhattacharya, Zhijie Chen, Christopher J. Jensen, Chen Liu

Interconnected magnetic nanowire (NW) networks offer a promising platform for 3-dimensional (3D) information storage and integrated neuromorphic computing. Here we report discrete propagation of magnetic states in interconnected Co nanowire networks driven by magnetic field and current, manifested in distinct magnetoresistance (MR) features. In these network

Yfke Dulek, Garazi Muguruza, Florian Speelman

When sending quantum information over a channel, we want to ensure that the message remains intact. Quantum error correction and quantum authentication both aim to protect (quantum) information, but approach this task from two very different directions: error-correcting codes protect against probabilistic channel noise and are meant to be very robust against

Conner Masteran, Ashutosh Kumar, Nakul Teke, Bimal Gaudel

This Comment identifies errors in the formalism/implementation reported in Ref. 1 and discusses some aspects of that work that were not fully specified in the original publication. This Comment also provides reference numerical results for a simple system to ease future implementation of the approach by other researchers.

Alice Moros, Mickael Denis Crouvizier, Ignacio Aviles Santillana, Stefano Sgobba

The design and production of Nb3Sn-based dipole and quadrupole magnets is critical for the realization of the High-Luminosity Large Hadron Collider (HL-LHC) at the European Organization for Nuclear Research (CERN). Nb3Sn superconducting coils are aimed at enhancing the bending and focusing strengths of accelerator magnets for HL-LHC and beyond. Due to the br

Inder Khatri, Arjun Choudhry, Aryaman Rao, Aryan Tyagi

Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing, and helps significantly in social media advertising. However, developing effective algorithms with minimal time complexity for real-world social netwo

Yifan Jiang, Peter Hedman, Ben Mildenhall, Dejia Xu

Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent effects, few efforts have been devoted to exploring its limits in a high-resolution setting. Specifically, existing NeRF-based methods face several limitations when reconstructing h

Caleb Bradford, Michael L. Nelson

Screenshots of social media posts have become common place on social media sites. While screenshots definitely serve a purpose, their ubiquity enables the spread of fabricated screenshots of posts that were never actually made, thereby proliferating misattribution disinformation. With the motivation of detecting this type of disinformation, we researched dev

Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom

Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimi

Shuo Chen

Uncertainty plays a crucial role in the machine learning field. Both model trustworthiness and performance require the understanding of uncertainty, especially for models used in high-stake applications where errors can cause cataclysmic consequences, such as medical diagnosis and autonomous driving. Accordingly, uncertainty decomposition and quantification

Gregory F. Snyder, Theodore Pena, L. Y. Aaron Yung, Caitlin Rose

We present and analyze a series of synthetic galaxy survey fields based on the IllustrisTNG Simulation suite. With the Illustris public data release and JupyterLab service, we generated a set of twelve lightcone catalogs covering areas from 5 to 365 square arcminutes, similar to several JWST Cycle 1 programs, including JADES, CEERS, PRIMER, and NGDEEP. From

Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala

Natural language often contains ambiguities that can lead to misinterpretation and miscommunication. While humans can handle ambiguities effectively by asking clarifying questions and/or relying on contextual cues and common-sense knowledge, resolving ambiguities can be notoriously hard for machines. In this work, we study ambiguities that arise in text-to-i

Siddharth P. Maharathy, Manimala Mitra, Agnivo Sarkar

We study a different variant of Left-Right Symmetric Model, incorporating Dirac type neutrinos. In the absence of the bi-doublet scalars, the possibility of a universal seesaw type of mass generation mechanism for all the Standard Model charged fermions have been discussed. The model has been constructed by extending the Standard Model particle spectrum with

Joshua R. Klein, Ana Machado, David Schmitz, Raimund Strauss

We discuss here future neutrino detectors with physics goals ranging from the eV to the EeV scale. The focus is on future enabling technologies for such detectors, rather than existing detectors or those under construction. The report includes methodologies across the broad spectrum of neutrino physics: liquid noble and other cryogenic detectors, includin LA

Yuan Zhang

Let $\Omega$ be a product domain in $\mathbb C^n, n\ge 2$, where each slice has smooth boundary. We observe that the canonical solution operator for the $\bar\partial$ equation on $\Omega$ is bounded in $W^{k,p}(\Omega)$, $k\in \mathbb Z^+, 1<p<\infty$. This Sobolev regularity is sharp in view of Kerzman-type examples.

Pha Nguyen, Kha Gia Quach, Chi Nhan Duong, Son Lam Phung

The development of autonomous vehicles generates a tremendous demand for a low-cost solution with a complete set of camera sensors capturing the environment around the car. It is essential for object detection and tracking to address these new challenges in multi-camera settings. In order to address these challenges, this work introduces novel Single-Stage G

Kyounghee Kim

The induced action on the Picard group of a rational surface automorphism with positive entropy can be identified with an element of the Coxeter group associated to $E_n, n\ge 10$ diagram. It follows that the set of dynamical degrees of rational surface automorphisms is a subset of the spectral radii of elements in the Coxeter group. This article concerns th

Eugenio Megias, Mariano Quiros

Using two different warped five-dimensional (5D) models with two branes along the extra dimension, we study the Green's functions and the spectral properties of some of the fields propagating in the bulk. While the first model has a discrete spectrum of Kaluza-Klein (KK) modes, the second one has a continuous spectrum above a mass gap. We also study the posi

Alireza Babaei, Jennifer Andreoli-Fang, Belal Hamzeh

With the exponential growth in mobile data traffic taking place currently and projected into the future, mobile operators need cost effective ways to manage the load of their networks. Traditionally, this has been achieved by offloading mobile traffic onto Wi-Fi networks due to their low cost and ubiquitous deployment. Recently, LTE operating in the unlicens

Mingliang Zhu, Antal Joós

It is known that $\sum\limits_{i=1}^{\infty} \frac{1}{i (i+1)} = 1$. In 1968, Meir and Moser asked for finding the smallest $\epsilon$ such that all the rectangles of sizes $1/i \times 1/(i + 1)$ for $i = 1, 2, \ldots$, can be packed into a unit square or a rectangle of area $1 + \epsilon$. In this paper, we show that we can pack the first $1.35\cdot10^{11}$

Manuel Caceres, Massimo Cairo, Brendan Mumey, Romeo Rizzi

A minimum path cover (MPC) of a directed acyclic graph (DAG) $G = (V,E)$ is a minimum-size set of paths that together cover all the vertices of the DAG. Computing an MPC is a basic polynomial problem, dating back to Dilworth's and Fulkerson's results in the 1950s. Since the size $k$ of an MPC (also known as the width) can be small in practical applications,

Tyler Ard, Longxiang Guo, Jihun Han, Yunyi Jia

Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase a

Aaryan Gupta, Inder Khatri, Arjun Choudhry, Pranav Chandhok

In recent years, social networking platforms have gained significant popularity among the masses like connecting with people and propagating ones thoughts and opinions. This has opened the door to user-specific advertisements and recommendations on these platforms, bringing along a significant focus on Influence Maximisation (IM) on social networks due to it

Shuo Chen, Shaikh Muhammad Uzair Norman

Cryptocurrencies, such as Bitcoin and Ethereum, are becoming increasingly prevalent mainly due to their anonymity, decentralization, transparency, and security. However, the completely public ledger makes the trace and analysis of each account possible as long as the identity behind the public address is revealed. Theoretically, social networks could make th

Zeno Capatti

The understanding of the physical laws determining the infrared behaviour of amplitudes is a longstanding and topical problem. In this paper, we show that energy conservation alone implies strong constraints on the threshold singularity structure of Feynman diagrams. In particular, we show that it implies a representation of loop integrals in terms of Fourie

Ramkumar Ramachandra

A theorem by Pridham and Lurie provides an equivalence between formal moduli problems and Lie algebras in characteristic zero. In his work, Lurie has distilled the axioms that the algebras appearing in the formal moduli problem need to satisfy, and worked out the case of $\mathbb{E}_\infty$-algebras using an incarnation of the Koszul duality, in the setting

Todd Mendenhall, Zi-Wei Lin

At low to moderate collision energies where the parton formation time $\tau_F$ is not small compared to the nuclear crossing time, the finite nuclear thickness significantly affects the energy density $\epsilon(t)$ and net conserved-charge densities such as the net-baryon density $n_B(t)$ produced in heavy ion collisions. As a result, at low to moderate ener

Arnaud Zaslavsky

Solar wind heating rates have often been calculated by fitting plasma and magnetic field data with a set of model functions. In this letter, we show that the rates obtained by such an approach strongly depend on the rather arbitrary choice one makes for these model functions. An alternative approach, consisting in monitoring the radial evolution of the adiab

Gerasimos Rigopoulos, Ashley Wilkins

We use Functional Renormalisation Group (FRG) techniques to analyse the behaviour of a spectator field, $\sigma$, during inflation that obeys an overdamped Langevin equation. We briefly review how a derivative expansion of the FRG can be used to obtain Effective Equations of Motion (EEOM) for the one- and two-point function and derive the EEOM for the three-

Xiao Wang, Zongzhen Wu, Bo Jiang, Zhimin Bao

The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption. Meanwhile, the biologically inspired event cameras attracted great interest due to their unique features, such as high dynamic range, dense temporal but sparse

Jan Ole Ernst, Felix Tennie

Mode entanglement in many-body quantum systems is an active area of research. It provides crucial insight into the suitability of many-body systems for quantum information processing tasks. Local super-selection rules must be taken into account when assessing the amount of physically accessible entanglement. This requires amending well-established entangleme

Shizhe Chen, Pierre-Louis Guhur, Makarand Tapaswi, Cordelia Schmid

Localizing objects in 3D scenes based on natural language requires understanding and reasoning about spatial relations. In particular, it is often crucial to distinguish similar objects referred by the text, such as "the left most chair" and "a chair next to the window". In this work we propose a language-conditioned transformer model for grounding 3D object